Summary

Summary {data-width=650}

Manhattan plot

manhattan_plot

manhattan_plot

QQ plot

qq_plot

qq_plot

AF plot

af_plot

af_plot

P-Z plot

pz_plot

pz_plot

beta_std plot

beta_std_plot

beta_std_plot

Metadata

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    "FORMAT.1": "<ID=SE,Number=A,Type=Float,Description=\"Standard error of effect size estimate\">",
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    "FORMAT.5": "<ID=EZ,Number=A,Type=Float,Description=\"Z-score provided if it was used to derive the EFFECT and SE fields\">",
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    "gwas_harmonisation_command": "--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/vcf_09_19b/bgzip_vcf/data.batch_5087.vcf.gz --id UKB-b:10250 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_5087.txt.gz --cohort_controls 99380 --ref /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/human_g1k_v37.fasta --json /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/ukb_gwas.json; 1.1.1",
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}

LDSC

*********************************************************************
* LD Score Regression (LDSC)
* Version 1.0.1
* (C) 2014-2019 Brendan Bulik-Sullivan and Hilary Finucane
* Broad Institute of MIT and Harvard / MIT Department of Mathematics
* GNU General Public License v3
*********************************************************************
Call: 
./ldsc.py \
--h2 /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-10250/UKB-b-10250_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-10250/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:40:19 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-10250/UKB-b-10250_data.vcf.gz ...
Read summary statistics for 9022059 SNPs.
Dropped 8796 SNPs with duplicated rs numbers.
Reading reference panel LD Score from ../reference/eur_w_ld_chr/[1-22] ...
Read reference panel LD Scores for 1290028 SNPs.
Removing partitioned LD Scores with zero variance.
Reading regression weight LD Score from ../reference/eur_w_ld_chr/[1-22] ...
Read regression weight LD Scores for 1290028 SNPs.
After merging with reference panel LD, 1287270 SNPs remain.
After merging with regression SNP LD, 1287270 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0354 (0.0054)
Lambda GC: 1.0751
Mean Chi^2: 1.0798
Intercept: 1.0111 (0.0065)
Ratio: 0.1394 (0.0819)
Analysis finished at Thu Oct 17 14:41:55 2019
Total time elapsed: 1.0m:36.07s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9479,
    "inflation_factor": 1.0475,
    "mean_EFFECT": -0,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 5,
    "n_p_sig": 72,
    "n_mono": 0,
    "n_ns": 0,
    "n_mac": 0,
    "is_snpid_unique": true,
    "n_miss_EFFECT": 0,
    "n_miss_SE": 0,
    "n_miss_PVAL": 0,
    "n_miss_AF": 0,
    "n_miss_AF_reference": 94053,
    "n_est": "NA",
    "ratio_se_n": "NA",
    "mean_diff": "NaN",
    "ratio_diff": "NaN",
    "sd_y_est1": "NaN",
    "sd_y_est2": "NA",
    "r2_sum1": 0,
    "r2_sum2": 0,
    "r2_sum3": 0,
    "r2_sum4": 0,
    "ldsc_nsnp_merge_refpanel_ld": 1287270,
    "ldsc_nsnp_merge_regression_ld": 1287270,
    "ldsc_observed_scale_h2_beta": 0.0354,
    "ldsc_observed_scale_h2_se": 0.0054,
    "ldsc_intercept_beta": 1.0111,
    "ldsc_intercept_se": 0.0065,
    "ldsc_lambda_gc": 1.0751,
    "ldsc_mean_chisq": 1.0798,
    "ldsc_ratio": 0.1391
}

Flags

name value
af_correlation FALSE
inflation_factor FALSE
n TRUE
is_snpid_non_unique FALSE
mean_EFFECT_nonfinite FALSE
mean_EFFECT_05 FALSE
mean_EFFECT_01 FALSE
mean_chisq FALSE
n_p_sig FALSE
miss_EFFECT FALSE
miss_SE FALSE
miss_PVAL FALSE
ldsc_ratio FALSE
ldsc_intercept_beta FALSE
n_clumped_hits FALSE
r2_sum1 FALSE
r2_sum2 FALSE
r2_sum3 FALSE
r2_sum4 FALSE

Definitions

General metrics

  • af_correlation: Correlation coefficient between AF and AF_reference.
  • inflation_factor (lambda): Genomic inflation factor.
  • mean_EFFECT: Mean of EFFECT size.
  • n: Maximum value of reported sample size across all SNPs, \(n\).
  • n_clumped_hits: Number of clumped hits.
  • n_snps: Number of SNPs
  • n_p_sig: Number of SNPs with pvalue below 5e-8.
  • n_mono: Number of monomorphic (MAF == 1 or MAF == 0) SNPs.
  • n_ns: Number of SNPs with nonsense values:
    • alleles other than A, C, G or T.
    • P-values < 0 or > 1.
    • negative or infinite standard errors (<= 0 or = Infinity).
    • infinite beta estimates or allele frequencies < 0 or > 1.
  • n_mac: Number of cases where MAC (\(2 \times N \times MAF\)) is less than 6.
  • is_snpid_unique: true if the combination of ID REF ALT is unique and therefore no duplication in snpid.
  • n_miss_<*>: Number of NA observations for <*> column.

se_n metrics

  • n_est: Estimated sample size value, \(\widehat{n}\).
  • ratio_se_n: \(\texttt{ratio_se_n} = \frac{\sqrt{\widehat{n}}}{\sqrt{n}}\). We expect ratio_se_n to be 1. When it is not 1, it implies that the trait did not have a variance of 1, the reported sample size is wrong, or that the SNP-level effective sample sizes differ markedly from the reported sample size.
  • mean_diff: \(\texttt{mean_diff} = \sum_{j} \frac{\widehat{\beta_j^{std}} - \beta_j}{\texttt{n_snps}}\), mean difference between the standardised beta, predicted from P-values, and the observed beta. The difference should be very close to zero if trait has a variance of 1.
    • \(\widehat{\beta_j^{std}} = \sqrt{\frac{{z}_j^2 / ({z}_j^2 + n -2)}{2 \times {MAF}_j \times (1 - {MAF}_j)}} \times sign({z}_j)\),
    • \({z}_j = \frac{\beta_j}{{se}_j}\),
    • and \(\beta_j\) is the reported effect size.
  • ratio_diff: \(\texttt{ratio_diff} = |\frac{\texttt{mean_diff}}{\texttt{mean_diff2}}|\), absolute ratio between the mean of diff and the mean of diff2 (expected difference between the standardised beta predicted from P-values, and the standardised beta derived from the observed beta divided by the predicted SD; NOT reported). The ratio should be close to 1. If different from 1, then implies that the betas are not in a standard deviation scale.
    • \(\texttt{mean_diff2} = \sum_{j} \frac{\widehat{\beta_j^{std}} - \beta^{\prime}_j}{\texttt{n_snps}}\)
    • \(\beta^{\prime}_j = \frac{\beta_j}{\widehat{\texttt{sd2}}_{y}}\)
  • sd_y_est1: The standard deviation for the trait inferred from the reported sample size, median standard errors for the SNP-trait assocations and SNP variances.
    • \(\widehat{\texttt{sd1}}_{y} = \frac{\sqrt{n} \times median({se}_j)}{C}\),
    • \(C = median(\frac{1}{\sqrt{2 \times {MAF}_j \times (1 - {MAF}_j)}})\),
    • and \({se}_j\) is the reported standard error.
  • sd_y_est2: The standard deviation for the trait inferred from the reported sample size, Z statistics for the SNP-trait effects (beta/se) and allele frequency.
    • \(\widehat{\texttt{sd2}}_{y} = median(\widehat{sd_j})\),
    • \(\widehat{sd_j} = \frac{\beta_j}{\widehat{\beta_j^{std}}}\),

r2 metrics

Sum of variance explained, calculated from the clumped top hits sample.

  • r2_sum<*>: r2 statistics under various assumptions
    • 1: \(r^2 = \sum_j{\frac{2 \times \beta_j^2 \times {MAF}_j \times (1 - {MAF}_j)}{\texttt{var1}}}\), \(\texttt{var1} = 1\).
    • 2: \(r^2 = \sum_j{\frac{2 \times \beta_j^2 \times {MAF}_j \times (1 - {MAF}_j)}{\texttt{var2}}}\), \(\texttt{var2} = {\widehat{\texttt{sd1}}_{y}}^2\),
    • 3: \(r^2 = \sum_j{\frac{2 \times \beta_j^2 \times {MAF}_j \times (1 - {MAF}_j)}{\texttt{var3}}}\), \(\texttt{var3} = {\widehat{\texttt{sd2}}_{y}}^2\),
    • 4: \(r^2 = \sum_j{\frac{F_j}{F_j + n - 2}}\), \(F = \frac{\beta_j^2}{{se}_j^2}\).

LDSC metrics

Metrics from LD regression

  • ldsc_nsnp_merge_refpanel_ld: Number of remaining SNPs after merging with reference panel LD.
  • ldsc_nsnp_merge_regression_ld: Number of remaining SNPs after merging with regression SNP LD.
  • ldsc_observed_scale_h2_{beta,se} Coefficient value and SE for total observed scale h2.
  • ldsc_intercept_{beta,se}: Coefficient value and SE for intercept. Intercept is expected to be 1.
  • ldsc_lambda_gc: Lambda GC statistics.
  • ldsc_mean_chisq: Mean \(\chi^2\) statistics.
  • ldsc_ratio: \(\frac{\texttt{ldsc_intercept_beta} - 1}{\texttt{ldsc_mean_chisq} - 1}\), the proportion of the inflation in the mean \(\chi^2\) that the LD Score regression intercepts ascribes to causes other than polygenic heritability. The value of ratio should be close to zero, though in practice values of 0.1-0.2 are not uncommon, probably due to sample/reference LD Score mismatch or model misspecification (e.g., low LD variants have slightly higher \(h^2\) per SNP).

Flags

When a metric needs attention, the flag should return TRUE.

  • af_correlation: abs(af_correlation) < 0.7.
  • inflation_factor: inflation_factor > 1.2.
  • n: n (max reported sample size) < 10000.
  • is_snpid_non_unique: NOT is_snpid_unique.
  • mean_EFFECT_nonfinite: mean(EFFECT) is NA, NaN, or Inf.
  • mean_EFFECT_05: abs(mean(EFFECT)) > 0.5.
  • mean_EFFECT_01: abs(mean(EFFECT)) > 0.1.
  • mean_chisq: ldsc_mean_chisq > 1.3 or ldsc_mean_chisq < 0.7.
  • n_p_sig: n_p_sig > 1000.
  • miss_<*>: n_miss_<*> / n_snps > 0.01.
  • ldsc_ratio: ldsc_ratio > 0.5
  • ldsc_intercept_beta: ldsc_intercept_beta > 1.5
  • n_clumped_hits: n_clumped_hits > 1000
  • r2_sum<*>: r2_sum<*> > 0.5

Plots

  • Manhattan plot
    • Red line: \(-log_{10}^{5 \times 10^{-8}}\)
    • Blue line: \(-log_{10}^{5 \times 10^{-5}}\)
  • QQ plot
  • AF plot
  • P-Z plot
  • beta_std plot: Scatter plot between \(\widehat{\beta_j^{std}}\) and \(\beta_j\)

Diagnostics

Details

Summary stats

skim_type skim_variable n_missing complete_rate character.min character.max character.empty character.n_unique character.whitespace logical.mean logical.count numeric.mean numeric.sd numeric.p0 numeric.p25 numeric.p50 numeric.p75 numeric.p100 numeric.hist
character ID 0 1.0000000 3 58 0 9013303 0 NA NA NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 1 0 4 0 NA NA NA NA NA NA NA NA NA NA
character ALT 0 1.0000000 1 1 0 4 0 NA NA NA NA NA NA NA NA NA NA
logical N 9022059 0.0000000 NA NA NA NA NA NaN : NA NA NA NA NA NA NA NA
numeric CHROM 0 1.0000000 NA NA NA NA NA NA NA 8.643024e+00 5.758175e+00 1.0000000 4.000000e+00 8.000000e+00 1.300000e+01 2.200000e+01 ▇▅▃▂▂
numeric POS 0 1.0000000 NA NA NA NA NA NA NA 7.878407e+07 5.633858e+07 828.0000000 3.242955e+07 6.934521e+07 1.145363e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -4.850000e-05 1.501690e-02 -0.1701220 -5.961300e-03 -3.080000e-05 5.834700e-03 1.970210e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.194160e-02 8.776900e-03 0.0043259 5.157800e-03 7.909400e-03 1.638390e-02 1.000160e-01 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.906078e-01 2.914124e-01 0.0000000 2.399999e-01 4.899999e-01 7.400005e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.906096e-01 2.913865e-01 0.0000000 2.361393e-01 4.872734e-01 7.431211e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.198991e-01 2.585027e-01 0.0035220 2.035600e-02 1.010910e-01 3.467070e-01 9.964780e-01 ▇▂▁▁▁
numeric AF_reference 94053 0.9895752 NA NA NA NA NA NA NA 2.202378e-01 2.504037e-01 0.0000000 1.777160e-02 1.186100e-01 3.452480e-01 1.000000e+00 ▇▂▁▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 49298 rs200943160 T C 0.0009446 0.0079872 0.9100000 0.9058567 0.623633 0.7821490 NA
1 54676 rs2462492 C T 0.0010289 0.0079361 0.9000000 0.8968453 0.398783 NA NA
1 86028 rs114608975 T C -0.0130417 0.0125865 0.2999998 0.3001248 0.104060 0.0277556 NA
1 91536 rs6702460 G T 0.0246503 0.0078020 0.0016000 0.0015806 0.455520 0.4207270 NA
1 234313 rs8179466 C T -0.0239125 0.0152784 0.1199999 0.1175552 0.074819 NA NA
1 534192 rs6680723 C T 0.0034623 0.0089248 0.6999999 0.6980618 0.240535 NA NA
1 546697 rs12025928 A G 0.0056593 0.0110597 0.6100002 0.6088577 0.912839 NA NA
1 693731 rs12238997 A G -0.0008666 0.0074251 0.9100000 0.9070858 0.117660 0.1417730 NA
1 705882 rs72631875 G A 0.0036744 0.0108603 0.7400005 0.7351156 0.067625 0.0315495 NA
1 706368 rs55727773 A G 0.0025145 0.0055021 0.6499995 0.6476644 0.514166 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A -0.0002642 0.0066584 0.9699999 0.9683445 0.137184 0.2052720 NA
22 51219387 rs9616832 T C 0.0154628 0.0086797 0.0749998 0.0748338 0.072570 0.0654952 NA
22 51219704 rs147475742 G A 0.0198678 0.0115388 0.0850002 0.0851011 0.041839 0.0473243 NA
22 51221190 rs369304721 G A 0.0168526 0.0116148 0.1499999 0.1467912 0.049067 NA NA
22 51221731 rs115055839 T C 0.0169353 0.0086823 0.0510000 0.0511111 0.072098 0.0625000 NA
22 51222100 rs114553188 G T -0.0218824 0.0101454 0.0309999 0.0310151 0.054522 0.0880591 NA
22 51223637 rs375798137 G A -0.0212638 0.0101985 0.0369999 0.0370699 0.054135 0.0788738 NA
22 51229805 rs9616985 T C 0.0160994 0.0087139 0.0649995 0.0646666 0.071960 0.0730831 NA
22 51232488 rs376461333 A G -0.0369631 0.0205168 0.0719996 0.0716081 0.020058 NA NA
22 51237063 rs3896457 T C -0.0008273 0.0052952 0.8800001 0.8758510 0.298287 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623633 ES:SE:LP:AF:ID  0.00094461:0.00798715:0.0409586:0.623633:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.398783 ES:SE:LP:AF:ID  0.0010289:0.00793613:0.0457575:0.398783:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.10406  ES:SE:LP:AF:ID  -0.0130417:0.0125865:0.522879:0.10406:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.45552  ES:SE:LP:AF:ID  0.0246503:0.00780204:2.79588:0.45552:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074819 ES:SE:LP:AF:ID  -0.0239125:0.0152784:0.920819:0.074819:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240535 ES:SE:LP:AF:ID  0.00346229:0.00892485:0.154902:0.240535:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.912839 ES:SE:LP:AF:ID  0.0056593:0.0110597:0.21467:0.912839:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.11766  ES:SE:LP:AF:ID  -0.000866626:0.00742514:0.0409586:0.11766:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067625 ES:SE:LP:AF:ID  0.00367435:0.0108603:0.130768:0.067625:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.514166 ES:SE:LP:AF:ID  0.00251452:0.00550212:0.187087:0.514166:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033703 ES:SE:LP:AF:ID  -0.00524385:0.0137232:0.154902:0.033703:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037357 ES:SE:LP:AF:ID  -0.00562679:0.0124834:0.187087:0.037357:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037443 ES:SE:LP:AF:ID  -0.00626073:0.0124426:0.21467:0.037443:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.037141 ES:SE:LP:AF:ID  -0.00641427:0.0125275:0.21467:0.037141:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.01673  ES:SE:LP:AF:ID  -0.0292493:0.0193278:0.886057:0.01673:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037704 ES:SE:LP:AF:ID  -0.00581068:0.012389:0.19382:0.037704:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037793 ES:SE:LP:AF:ID  -0.00649547:0.0123504:0.221849:0.037793:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101386 ES:SE:LP:AF:ID  0.000753833:0.00909361:0.0315171:0.101386:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.958003 ES:SE:LP:AF:ID  0.00517111:0.0118807:0.180456:0.958003:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031821 ES:SE:LP:AF:ID  0.0157831:0.0217296:0.327902:0.031821:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052577 ES:SE:LP:AF:ID  -0.00586632:0.0175571:0.130768:0.052577:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.037251 ES:SE:LP:AF:ID  -0.00611247:0.0124373:0.207608:0.037251:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037567 ES:SE:LP:AF:ID  -0.00689676:0.012332:0.236572:0.037567:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.84101  ES:SE:LP:AF:ID  0.00166038:0.00642966:0.09691:0.84101:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056088 ES:SE:LP:AF:ID  0.000789941:0.0104656:0.0268721:0.056088:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.123562 ES:SE:LP:AF:ID  -0.000506028:0.00704946:0.0268721:0.123562:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025857 ES:SE:LP:AF:ID  -0.00341891:0.0173094:0.0757207:0.025857:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122759 ES:SE:LP:AF:ID  -0.000171785:0.00705305:0.00877392:0.122759:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.133585 ES:SE:LP:AF:ID  2.77144e-05:0.00694993:-0:0.133585:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011246 ES:SE:LP:AF:ID  0.0175901:0.0251425:0.318759:0.011246:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.006015 ES:SE:LP:AF:ID  -0.00856744:0.0316603:0.102373:0.006015:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.03754  ES:SE:LP:AF:ID  -0.00560261:0.0121948:0.187087:0.03754:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.836798 ES:SE:LP:AF:ID  0.00302004:0.00622173:0.200659:0.836798:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.83637  ES:SE:LP:AF:ID  0.00240443:0.00621467:0.154902:0.83637:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.868134 ES:SE:LP:AF:ID  0.00148712:0.00666838:0.0861861:0.868134:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.131522 ES:SE:LP:AF:ID  -0.000962682:0.00668279:0.05061:0.131522:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037983 ES:SE:LP:AF:ID  -0.00523328:0.0120029:0.180456:0.037983:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038232 ES:SE:LP:AF:ID  -0.00593523:0.0119282:0.207608:0.038232:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.867441 ES:SE:LP:AF:ID  0.00110902:0.00665497:0.0604807:0.867441:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.867546 ES:SE:LP:AF:ID  0.00093023:0.00665814:0.05061:0.867546:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038181 ES:SE:LP:AF:ID  -0.00562024:0.0119764:0.19382:0.038181:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.86744  ES:SE:LP:AF:ID  0.00119815:0.00665455:0.0655015:0.86744:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.00508  ES:SE:LP:AF:ID  -0.0330742:0.0344858:0.468521:0.00508:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005048 ES:SE:LP:AF:ID  -0.03368:0.0345817:0.481486:0.005048:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.835914 ES:SE:LP:AF:ID  0.00295562:0.00620136:0.200659:0.835914:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.038198 ES:SE:LP:AF:ID  -0.00538104:0.0119925:0.187087:0.038198:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.836519 ES:SE:LP:AF:ID  0.00281076:0.00621805:0.187087:0.836519:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013186 ES:SE:LP:AF:ID  0.0162224:0.0222977:0.327902:0.013186:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005526 ES:SE:LP:AF:ID  -0.0289862:0.0336548:0.408935:0.005526:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.837809 ES:SE:LP:AF:ID  0.00307645:0.00630326:0.200659:0.837809:rs3131965